Encoding and analyzing aerial imagery using geospatial semantic graphs

Technical Report · Sat Feb 01 00:00:00 EST 2014

DOI:https://doi.org/10.2172/1204099· OSTI ID:1204099

Watson, Jean-Paul ^[1]; Strip, David R. ^[1]; McLendon, William C. ^[1]; Parekh, Ojas D. ^[1]; Diegert, Carl F. ^[1]; Martin, Shawn Bryan ^[1]; Rintoul, Mark Daniel ^[1]

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1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

While collection capabilities have yielded an ever-increasing volume of aerial imagery, analytic techniques for identifying patterns in and extracting relevant information from this data have seriously lagged. The vast majority of imagery is never examined, due to a combination of the limited bandwidth of human analysts and limitations of existing analysis tools. In this report, we describe an alternative, novel approach to both encoding and analyzing aerial imagery, using the concept of a geospatial semantic graph. The advantages of our approach are twofold. First, intuitive templates can be easily specified in terms of the domain language in which an analyst converses. These templates can be used to automatically and efficiently search large graph databases, for specific patterns of interest. Second, unsupervised machine learning techniques can be applied to automatically identify patterns in the graph databases, exposing recurring motifs in imagery. We illustrate our approach using real-world data for Anne Arundel County, Maryland, and compare the performance of our approach to that of an expert human analyst.

• Research Organization: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 1204099
• Report Number(s): SAND2014-1405; 504876
• Country of Publication: United States
• Language: English